Essential Python libraries which will save you a lot of time when dealing with data analysis and machine learning. I’ve listed the most used libraries and their main uses.

Enjoy!

**Numpy**

- Numerical Python, used for numerical computing.
**Fast multidimensional array object ndarray**- Operations between arrays
- Reading and writing array-based datasets to disk
- Linear algebra, fourier transform, random numbers
- C API to enable extensions and C or C++ code to access data structures and computational facilities

**Pandas**

- High level data structures and functions. Work with structured or tabular data fast and easy.
**DataFrame**– tabular, column-oriented data structured with both row and column label, and the**Series**, a one-dimensional labeled array object- NumPy + relational databases
- Reshape, slice and dice, aggregations, subsets of data
- Data structures with labeled axes supporting automatic or explicit data alignment
- Integrated time series functionality
- Same data structured to handle both time series and non-time series data
- Arithmetic operations and reductions that preserve metadata
- SQL functions
- Flexible handling of missing data

**Matplotlib**

- Plots and other two-dimensional data visualizations.

**Scipy**

- Collection of packages addressing a number of different standard problem domains
- scipy.integrate: numerical integration routines and differential equation solvers
- scipy.linalg: Linear algebra routines and matrix decompositions
- scipy.optimize: Function optimizers(minimizers) and root finding problems
- scipy.signals: signal processing tools
- scipy.sparse: sparse matrices and sparse linear system solvers
- scipy.special: SPECFUN, gamma function
- scipy.stats: continuous and discrete probability distributions (density functions, samplers, continuous distribution functions), various statistical tests and more descriptive statistics

**Scikit-learn**

- Classification: nearest neighbors, random forest, logistic regressions, SVM…
- Regression: Lasso, ridge regression…
- Clustering: k-means, spectral clustering…
- Dimensionality reduction: PCA, feature selection, matrix factorization…
- Model selection: Grid search, cross validation…
- Preprocessing: feature extraction and normalization

**Statsmodels**

- Statistical analysis and econometrics
- Regression models: Linear regression, generalized linear models, robust linear models, linear mixed effect models…
- Analysis of variance
- Time series analysis
- Nonparametric methods: Kernel density estimation and regression
- Visualization
- Statistical inference, uncertainty and p-values